The Wishart distribution is a well-established statistical model for characterizing the density of random variables in Polarimetric SAR (PolSAR) data, particularly within homogeneous regions where Gaussian assumptions hold. However, as PolSAR applications expand into heterogeneous environments, alternative statistical models have been developed to better capture the complexity of such areas, playing an important role in tasks such as classification. In this study, we examine the effectiveness of covariance matrix estimation using the median matrix, a technique grounded in optimal transport theory and validated in prior research for its effectiveness. Building on this foundation, we propose the application of a statistical model tailored for heterogeneous regions, i.e., following theG0Pdistribution, addressing the limitations of traditional assumptions. This method is particularly suitable for high-resolution PolSAR datasets, where the homogeneity hypothesis often does not hold. The experimental results obtained using L-band PolSAR images acquired over Foulum in Denmark demonstrate the robustness of our proposed variant.

In recent years, generalized likelihood ratio test (GLRT) scatterers’ detection in the context of synthetic aperture radar tomography (TomoSAR) has gained great interest from the remote sensing scientific community. This is due to its effectiveness in identifying scatterers within each single azimuth–range resolution cell, particularly in urban areas. The multilook GLRT (M-GLRT) variant offers more satisfactory results at the expense of spatial resolution deterioration, by jointly exploiting the neighboring information of the pixel to be reconstructed. In this context, coherent and incoherent formulations can be adopted. The former provides better performance in the assumption of constant reflectivity of the pixels in the considered neighborhood, while the latter is much more robust with respect to the violation of this assumption. In this article, an adaptive formulation of the coherent and incoherent GLRT is presented with the aim of improving scatterers’ detection and their height estimation. The method is based on an adaptive window setting, to select adjacent pixels with similar height and reflectivity characteristics. A detailed study and analysis of the proposed adaptive coherent M-GLRT (ACM-GLRT) detector has been conducted and validated through simulations along with comparison to both standard and adaptive formulations of incoherent M-GLRT. Experimental findings from a real dataset acquired by the German TerraSAR-X (TSX) over the city of Naples (Italy) demonstrate the performance improvement of our proposed approach.

The reliability of Tomographic SAR (TomoSAR) products depends on the quality of complex-valued tomographic interferograms. The latters are unfortunately affected by noise from various sources. In order to reduce their impact and hence improve the outcome, filtering methods can be implemented at different levels of TomoSAR process. In this paper, we propose the application of a contextual denoising approach in transformed domains, based on Subbands decomposition and non-linear weighting, with the aim to study its influence on TomosAR height and deformation velocity estimation using Generalized Likelihood Ratio Test detection. Both spatial and spatiotemporal arrangements of overlapping blocks are considered in wavelet domains. The non-linear filtering parameter is estimated from the coherence and/or pseudo-correlation indicators. In order to show the effectiveness of the approach, the obtained findings have been compared to the state-of-the-art methods namely Goldstein and Baran filters. The assessment of the results with respect to denoising and TomoSAR evaluation metrics was carried out using both simulated and real data acquired by TerraSAR-X satellite over the city of Naples.

Synthetic Aperture Radar Tomography has been an active research field for the estimation of both artificial structures and forest heights. Most spectral analysis approaches explore the covariance matrix (CM) to provide accurate reflectivity profiles. In practical situations, the statistical CM is unknown, hence several methods replace it with the sampling CM. Unfortunately, the latter does not always meet the application requirements. At our end, we propose to apply the Expectation Maximization algorithm to iteratively estimate the pseudo spectrum and then update the CM. The impact the proposed method has on height estimation over vegetated areas is carried out. Experimental results on a real dataset acquired by an airborne system show the effectiveness of the Expectation Maximization estimator in terms of ground and canopy discrimination for each polarization channel.

The detection of multiple scatterers within each resolution cell is an open research subject in synthetic aperture radar (SAR) tomography (TomoSAR). For over a decade, the generalized likelihood ratio test (GLRT) detector has been implemented along with its variants, allowing the generation of height maps and 3-D point clouds with good precision. However, they are limited by the grid search during the optimization of the maximum likelihood function. In order to mitigate this, we propose a gridless version of GLRT where the particle swarm optimization (PSO) method is used to locate the minima. The conducted analysis of the proposed detector with respect to the state-of-the-art methods behavior on simulated and real datasets proved the effectiveness of PSO-GLRT in terms of height accuracy and computational cost. The evaluation metrics, root-mean-square error (RMSE), accuracy, and completeness, have been used as a quantitative improvement indicator for estimated height assessment.

The reconstruction of high-elevation natural and artificial structures through Synthetic Aperture Radar (SAR) tomography has been an active research topic owing to its significance in various earth science applications. However, the complexity of this task arises from inaccuracies in the estimated reconstruction, attributed to factors such as low signal-to-noise ratios, decorrelations, few and uneven measurements, and overlapping scatterers. The utilization of iterative spectral estimation methods has been demonstrated to be beneficial in addressing some of these inaccuracies. Thus, selecting the best method within this class constitutes a challenge. In this context, our letter aims to propose a generalized formula linking the maximum likelihood-based iterative methods via a regularization parameter. The behavior of the latter is analyzed for several values in order to unveil the potential of the proposed approach in achieving a balance between noise reduction and detection performance. The experimental study has been conducted on simulated and real SAR data acquired by airborne and spaceborne systems covering tropical forest and build-up areas. The obtained results show the effectiveness and performance of the optimal regularization parameter to eliminate noise while preserving scatterers’ contribution.

Multi-baseline multi-temporal Synthetic Aperture Radar (SAR) techniques such as SAR Tomography (TomoSAR) are affected by different forms and sources of noise. Its presence in complex-valued interferograms alters the reliability and accuracy of height estimation results. Thus, the key challenge of TomoSAR is to identify scatterers interfering within the same resolution cell. To this aim, we propose a spectral contextual filtering method based on subbands decomposition to reduce noise influence and improve the quality of the interferometric product for TomoSAR application. The impact of the proposed pre-processing approach on the Generalized Likelihood Ratio Test-based detection is carried out. Experimental results on data acquired by TerraSAR-X sensor show the performances of the denoising method to increase the detection capabilities.

Synthetic aperture radar tomography (TomoSAR) has recently received particular interest from the remote-sensing community, due to its ability to provide 3-D reconstructions of environments with complex structures. Unfortunately, different forms of decorrelations and processing errors affect the quality of the resulting 2-D/3-D images. One way to cope with the impact of these nuisances is to apply appropriate filtering to the interferometric data stack as a preprocessing step. The first obstacle to be dealt with, especially in urban areas, is to define a filter whose parameters have to be set in such a way as to improve smoothing capabilities while preserving edges. To this aim, the main objective of this article is twofold: 1) the application of a spatio-temporal contextual filter whose parameters depend on 3-D quality indicators of the multibaseline interferometric image stack and 2) evaluation of the denoising effect on the application of nonparametric spectral estimation and detection algorithms. For that, we consider several quantitative metrics to assess, on the one hand, the filtering performances, and, on the other hand, its impact on the reflectivity function recovered from conventional tomographic inversion and detection methods. Experimental results from a set of TerraSAR-X (TSX) images highlight the efficiency of the filtering process by improving the scatterers’ detection and height localization, of a man-made structure.